Can a trading strategy be doing too well? That sounds counter-intuitive, certainly. Like suggesting that someone can be too rich or too successful. You might be thinking there’s no such thing. But when it comes to managing your trading strategy, one that is performing too well is a warning sign. That’s because, quite often, an over-achieving trading strategy can turn into a honey trap.

In this article we will focus on a couple of cases studies where a “too good” strategy is something to watch out for. Hopefully, that’s a lesson one can learn from now rather than, painfully, at some later date.

A Trading Strategy with a High Win Ratio

The first case study is one of a strategy of mid- to short-term timeframes. In this case study, the frequency of executed trades is higher than 100 a month.

Usually, a solid strategy has an average win ratio of 45% to 60%. That ratio might sometimes be lower if the risk reward ratio is very strong. But higher than that is almost always is unsuitable.

In the strategy below we can see that the average win ratio per month runs from as low as 44 to as high as 64. Sometimes it is on the rise in the higher range and sometimes in the lower range. Regardless which, eventually it will all revolve around the average.

For example, in the month of January 2014, the win ratio was 52%. That means that for every 100 trades executed, 52 were profitable.

But in the second part(red), we can see things start to go a bit too well. The win ratio jumped to above 80% and stayed above that level for 3 months. Naturally, at 80%, that was far beyond the norm. What would come next was inevitable.

After 3 months of more than 80% of profitable trades, the win ratio fell to roughly 20% for the following 3 months. Because a long-term win ratio always has a long-term expectancy of maximal 50-60%. That means that a prolonged period of a high win ratio can be followed by a prolonged period of a very low win ratio.

Why is it Risky?

First, if you had 3 months of roughly 20% win ratio means you lost 80% of the trades. That means that, assuming you trade 100 times a month, you lost 80 trades. That’s a pretty hard hit. And if you risk $50 in every trade you lost $6,000 on aggregate (80 losing, 20 profitable) after 3 months. That is a big blow.

The second reason it is risky is more psychological but still very common. That is that many traders fail to realize that an overly high win ratio is very temporary in nature. Having failed to see that, what do they do? They raise their leverage. And when the sweet turns to bitter and they have only a 20% win ratio for 3 months, then what? All they’re left with is an empty account.

What should you do? This is rather simple; you reduce the risk you take per trade by lowering your leverage. It’s true you will gain less but you will also avoid the pitfall that comes after. Of course, if your leverage is already low and your account can sustain a prolonged period of a low win ratio than let statistics reign. But be prepared, psychologically, for choppy times.

Trading Strategy Profits that are Too Much Too Fast

The second case study is common in trading strategies that are low in frequency. The durations may range from a few days to even a few months. Let’s say you enter into a buy trade after your trading strategy indicated a bullish trend. You set the limit at 1,290. On average, you expect the limit to be reached within 3 to 4 days. But suddenly the trade advances in a fraction of the time and reaches relatively close to your trade.

Why is it Risky?

Because many times traders insist on waiting for their limit to be hit so they leave the trade open. But then the pair moves into overbought territory and before you know it the trend has reversed. You either close the position with a much smaller gain or, even worse, you lose the trade. Because you trade at a low frequency, this one outcome can be quite painful. You lost precious time when your position was open and missed the potential profit that you could have made.

What should you do? The solution here is rather simple, as well. You could use a trailing stop loss which is the most obvious tool to avert such cases.

Alternatively, you could use oscillators to identify a potentially hazardous situation. Add a component to your trading strategy to alert you if your pair reaches an overbought (or oversold) level. Make it a practice to exit the trade even if the target was not reached.

The vast majority of traders obsess over the percent accuracy of their expert advisors. Intuition makes it seem like that the more often a trader wins, the greater the chances or turning a profit. Alas, such an approach ignores a critical variable.

The average win-loss ratio plays an equally vital role in determining the net outcome. I meet a lot of would be scalpers. High frequency trading is incredibly popular, but a lot of traders involved with it only do so because it puts easy points on the board. They don’t pursue a strategy because it has any positive expectation. In other words, they are gambling and not trading.

One of the reasons that I love trading so much, and why I generally dislike gambling, is that you are always in control of the potential payout and the payout ratio. When I play blackjack, I only control the risk and payout. I do not control the ratio of the payout at all. It’s always 1:1.

My decisions in blackjack can only realistically improve the odds to 50%. More than likely, my game play will lower the odds below that threshold. Making decisions repeatedly will overwhelmingly result in human error. It’s our nature.

When I open my forex account, each trade commences a new round in the game. The critical difference between trading and blackjack is that I control the ratio of the payout, plus I still control the risk and quantity of the payout. The net outcome can still move against me due to random chance. The key distinction is that the typical outcome should shift in my favor with an algorithmic trading system.

One of my favorite trading books is Van Tharp’s Trade Your Way To Financial Freedom. We’ll be talking about this one soon; it’s the next item on Jon Rackley’s reading list. One of my favorite aspects of the book is its emphasis on money management strategies and trade expectation.

The term money management connotes many things to many people. The more accurate phrase would be to describe it as a position sizing strategy. When entering a trade, you realistically need to know:

What is expected loss as a percentage of the account?

What is the expected gain as a percentage of the account?

What is the percent accuracy of my trades?

Answering these questions accurately leads to the decision of how many lots, contracts or shares to trade. Controlling the size leads to controlling the outcomes. When you control the outcomes, you ideally earn a profit for your efforts.

Fixed fractional money management

Notice that I said percentage of the account in the bulleted items and not the dollar value of the trade. Thinking in terms of dollars is easier on the mind. The problems is that it ignores the wonderful benefits of exponential growth.

Every financial advisor on earth warns you that compound interest, which is a form of exponential growth, is the strongest force working for you with investments or against you with debts. Applying the same concept to trading, you want to put the power of compound growth on your side.

The fixed fractional formula is an ugly way to telling you to use exponential growth in your trading strategy. Say, for example, that you elect to risk 1% of the trading account based on the distance to the stop loss. If you have a $10,000 trading account, that’s only $100 of risk. Say that the trade works out and that you made $100. The next trade should risk $101.

Try not to roll your eyes at that one. Risking an extra dollar seems trivial and nit picky. I assure you that it is not.

I’m really not sure how to explain how all those little differences add up, but they do. I wrote a money management calculator a few years back that calculated how fixed fractional money management affects returns. The little things really do add up. With a very slight probability of winning and 50:50 odds, the returns were overwhelmingly larger when using a fixed fractional approach instead of a fixed lot approach. You should increase the position size after winners and decrease the position size after losers.

Percent accuracy is half important

If I paid you $1 for every win and you win 99% of the time, should you play my game?

You don’t have enough information to make a decision yet. You need to find out what happens when you lose.

If you lose $100 or more on the trade that only loses 1 time in 100, you should never play my game. You will lose if you play too often. And no, there is no such thing as just playing ten times and stopping. You have the same risk of losing on the first trade as you do on the 100th. It’s not safe to play at all.

The only way that you should decide to play the game is if the total payout is better than even. The total result of wins equals 99 trades * $1/trade = $99. The one loss must be less than $99 to give me the green light on playing.

If I lose $80 one time and make $99 on the remaining trials, then I will have an average win loss ratio of $99/$80 = 1.24. A system like this would be wildly in my favor.

A 60% winning accuracy is a lot more likely to happen in the trading world. Let’s say that I make $100 on every winning trade. My total winning value is 60 trades (out of 100) * $100/trade = $6,000. The maximum average loss that this system could tolerate is:

The maximum average loss that we can tolerate is $6,000 / 40 trades = $150. I should consider trading this system if the average loss comes in at $149 or less. The smaller the average loss, the greater the net outcome.

Kelly formula for Forex Trading

One problem we face with money management strategies is choosing the percentage of the account to risk. The difference between risking 1% or risking 2% of the account equity is simply one of proportion. One of the options either provides a risk-reward profile suitable to the trader or it doesn’t. The larger the appetite for risk and reward, the bigger the number involved.

The Kelly formula removes the proportionality for the question and takes a different approach: how do I make the absolute largest sum of money over time using my trading statistics. The goal is to make the maximum amount of money without getting margin called.

The formula to use is K = W – (1-W)/R where:

K = percentage of capital to be put into a single trade.
W = Historical winning percentage of a trading system.
R = Historical Average Win/Loss ratio.

The approach is most suitable for those trading small accounts, perhaps those with only a few thousand dollars, that they want to grow with maximum aggression. Losing a few dollars is thoroughly unpleasant (been there, done that!), but it’s not financially devastating, either.

It’s important to keep in mind that the Kelly formula attempts to push the trading system to its absolute maximum without busting. Knowing how close it is to the edge of busting, it’s critically important that you understate the good assumptions and overstate the bad ones. Drop the expected percent accuracy by several percentage points to accommodate the chance of error. Lower the win:loss ratio for the same reason.

The easiest way to reduce error and the chance of acting too aggressively is to make sure that you calculated the EA’s percent accuracy and its win loss ratio on a large enough sample size. I would consider 100 trades as the absolute bare minimum. 300-400 is sufficient. 1,000+ trades makes for an adequate sample for most expert advisors and trading robots.

Of course, you can always take the easier approach and simply cut the Kelly formula’s risk suggestion in half. It adds a bit of scientific flair to the strategy, while minding the fact that we are human. Watching an account drop near zero will break the heart of even the most battle tested trader. It’s impossible to stop caring about drawdown, which the Kelly formula totally ignores.